Scale-free networks in cell biology

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human transcription factor (TF) genes encode multiple protein isoforms differing in DNA-binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators," both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

The transcriptome provides a highly informative molecular phenotype to connect genotype to phenotype and is most frequently measured by RNA-sequencing (RNA-seq). Therefore, an ultimate goal is to perturb every gene and measure changes in the transcriptome. However, this remains challenging, especially in intact organisms due to different experimental and computational challenges. Here, we present 'Worm Perturb-Seq (WPS)', which provides high-resolution RNA-seq profiles for hundreds of replicate perturbations at a time in a living animal. WPS introduces multiple experimental advances that combine strengths of bulk and single cell RNA-seq, and that further provides an analytical framework, EmpirDE, that leverages the unique power of the large WPS datasets. EmpirDE identifies differentially expressed genes (DEGs) by using gene-specific empirical null distributions, rather than control conditions alone, thereby systematically removing technical biases and improving statistical rigor. We applied WPS to 103 Caenhorhabditis elegans nuclear hormone receptors (NHRs) to delineate a Gene Regulatory Network (GRN) and found that this GRN presents a striking 'pairwise modularity' where pairs of NHRs regulate shared target genes. We envision that the experimental and analytical advances of WPS should be useful not only for C. elegans, but will be broadly applicable to other models, including human cells.

Cardiac ATP production and contractility are favorably regulated by short-term S100A9 blockade after myocardial infarction

INTRODUCTION

The infarcted heart is energetically compromised exhibiting a deficient production of adenosine triphosphate (ATP) and the ensuing impaired contractile function. Short-term blockade of the protein S100A9 improves cardiac performance in mice after myocardial infarction (MI). The implications upon ATP production during this process are not known.

OBJECTIVES

This study evaluates whether S100A9 blockade effects ATP synthesis and cardiac contractility in C57BL/6 mice at seven days post-MI.

METHODS

Three experimental groups were used: (i) mice with MI, induced by permanent left coronary ligation, (ii) mice with MI, short-term treated with the S100A9 blocker ABR-238901, and (iii) sham (control) mice. After removing the left ventricle, mass spectrometry, pathway enrichment analysis, Western blot, RT-PCR and pharmacological network analysis were performed.

RESULTS

A number of 600 differentially abundant proteins (DAPs) was significantly altered by the S100A9 blocker in MI-treated mice compared with MI mice. Some of these proteins were associated with oxidative phosphorylation, citrate cycle (TCA), mitochondrial fatty acid beta-oxidation, glycolysis and cardiac muscle contraction pathways. In the ischemic ventricle, ABR-238901 treatment increased (1.8- to 38-fold) the abundance of proteins NDUFAB1, UQCRC1, HADHA, ACAA2, ALDOA, PKM1, DLD, DLAT, PDHX, ACO2, IDH3A, FH1, CKM, CKMT2, TNNC1, crucial for early cellular metabolic changes, ATP distribution and contractility. The cardiac level of ATP increased (1.8-fold, p < 0.05) in MI mice treated with ABR-238901 compared to MI mice. The network pharmacology analysis uncovered potential pharmacologic targets of ABR-238901 that may interact with DAPs related to ATP production and contractility.

CONCLUSION

Short-term S100A9 blockade effectively regulates the proteins implicated in ATP production and cardiac contractility post-MI, providing a framework for future cardiac energy metabolism studies.

InTiCAR: Network-based identification of significant inter-tissue communicators for autoimmune diseases

Inter-tissue communicators (ITCs) are intricate and essential aspects of our body, as they are the keepers of homeostatic equilibrium. It is no surprise that the dysregulation of the exchange between tissues are at the core of various disorders. Among such conditions, autoimmune diseases (AIDs) refer to a collection of pathological conditions where the miscommunication drives the immune system to mistakenly attack one's own body. Due to their myriad and diverse pathophysiologies, AIDs cannot be easily diagnosed or treated, and continuous efforts are required to seek for potential diagnostic markers or therapeutic targets. The identification of ITCs with significant involvement in the disease states is therefore crucial. Here, we present InTiCAR, Inter-Tissue Communicators for Autoimmune diseases by Random walk with restart, which is a network exploration-based analysis method that suggests disease-specific ITCs based on prior knowledge of disease genes, without the need for the external expression data. We first show that distinct ITC profile s can be acquired for various diseases by InTiCAR. We further illustrate that, for autoimmune diseases (AIDs) specifically, the disease-specific ITCs outperform disease genes in diagnosing patients using the UK Biobank plasma proteome dataset. Also, through CMap LINCS dataset, we find that high perturbation on the AIDs genes can be observed by the disease-specific ITCs. Our results provide and highlight unique perspectives on biological network analysis by focusing on the entities of extracellular communications.

Analysis of type 2 diabetes mellitus-related genes by constructing the pathway-based weighted network

Complex network is an effective approach to studying complex diseases, and provides another perspective for understanding their pathological mechanisms by illustrating the interactions between various factors of diseases. Type 2 diabetes mellitus (T2DM) is a complex polygenic metabolic disease involving genetic and environmental factors. By combining the complex network approach with biological data, this study constructs a pathway-based weighted network model of T2DM-related genes to explore the interrelationships between genes, here a weight is assigned to each edge in terms of the number of the same pathways in which the two nodes (genes) connected to the edge are involved. The edge weights can reflect differences in the strength of connections (interactions) between nodes (genes), which intuitively reflect the extent of biological correlations between genes and contribute to the importance of the nodes. Analysis of statistical and topological characteristics shows that the edge weights are correlated to the network topology, and the edge weight distribution decays as a power-law. The disparity of the weights indicates that the edge weight distribution for the nodes with the same degree is of approximately equal weights; and most edges with the higher weights tend to connect with the higher degree nodes. To determine the key hub genes of the weighted network, an integrated ranking index is used to comprehensively reflect the contribution of the three indices (strength, degree and number of pathways) of nodes; by taking the threshold of integrated ranking index greater than 0.56, 12 key hub genes are identified: MAPK1, PIK3CD, PIK3CA, PIK3R1, AKT2, AKT1, KRAS, TNF, MAPK8, PRKCA, IL6 and MTOR. These genes should play an important role in the occurrence and development of T2DM, and can be regarded as potential therapeutic targets for further biological and medical research on their functions in T2DM. It can be expected that combining complex network approach with other data analysis techniques can provide more clues for exploring the pathogenesis and treatment of T2DM and other complex diseases in the future.

Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives

PURPOSE OF REVIEW

Heart failure (HF) is often accompanied by a constellation of comorbidities, leading to diverse patient presentations and clinical trajectories. While traditional methods have provided valuable insights into our understanding of HF, network medicine approaches seek to leverage these complex relationships by analyzing disease at a systems level. This review introduces the concepts of network medicine and explores the use of comorbidity networks to study HF and heart disease.

RECENT FINDINGS

Comorbidity networks are used to understand disease trajectories, predict outcomes, and uncover potential molecular mechanisms through identification of genes and pathways relevant to comorbidity. These networks have shown the importance of non-cardiovascular comorbidities to the clinical journey of patients with HF. However, the community should be aware of important limitations in developing and implementing these methods. Network approaches hold promise for unraveling the impact of comorbidities in the complex presentation and genetics of HF. Methods that consider comorbidity presence and timing have the potential to help optimize management strategies and identify pathophysiological mechanisms.

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. To explore the pathways and biological functions these genes are involved in, KEGG and Gene Ontology (GO) enrichment analyses on the disclosed gene sets were performed, highlighting the significance of the genes selected across several relevent pathways and GO terms. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed.

Quantitative omnigenic model discovers interpretable genome-wide associations

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects.

Where physics and biology meet

As part of this special issue on physics and biology, we invited several leading experts that bridge these disciplines to provide their views on the reciprocal contributions of each field and the benefits and challenges of working across physics and biology: introduction provided by Wallace Marshall.

Inference of single-cell network using mutual information for scRNA-seq data analysis

BACKGROUND

With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.

RESULTS

We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.

CONCLUSIONS

SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM .

Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks

BACKGROUND AND OBJECTIVE

Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand.

METHODS

MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance.

RESULTS

MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner.

CONCLUSIONS

MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.

GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data

Single-cell data is increasingly used for gene regulatory network (GRN) inference, and benchmarks for this have been developed based on simulated data. However, existing single-cell simulators cannot model the effects of gene perturbations. A further challenge lies in generating large-scale GRNs that often struggle with computational and stability issues. We present GeneSPIDER2, an update of the GeneSPIDER MATLAB toolbox for GRN benchmarking, inference, and analysis. Several software modules have improved capabilities and performance, and new functionalities have been added. A major improvement is the ability to generate large GRNs with biologically realistic topological properties in terms of scale-free degree distribution and modularity. Another major addition is a simulation of single-cell data, which is becoming increasingly popular as input for GRN inference. Specifically, we introduced the unique feature to generate single-cell data based on genetic perturbations. Finally, the simulated single-cell data was compared to real single-cell Perturb-seq data from two cell lines, showing that the synthetic and real data exhibit similar properties.

A comparative analysis of ENCODE and Cistrome in the context of TF binding signal

BACKGROUND

With the rise of publicly available genomic data repositories, it is now common for scientists to rely on computational models and preprocessed data, either as control or to discover new knowledge. However, different repositories adhere to the different principles and guidelines, and data processing plays a significant role in the quality of the resulting datasets. Two popular repositories for transcription factor binding sites data - ENCODE and Cistrome - process the same biological samples in alternative ways, and their results are not always consistent. Moreover, the output format of the processing (BED narrowPeak) exposes a feature, the signalValue, which is seldom used in consistency checks, but can offer valuable insight on the quality of the data.

RESULTS

We provide evidence that data points with high signalValue(s) (top 25% of values) are more likely to be consistent between ENCODE and Cistrome in human cell lines K562, GM12878, and HepG2. In addition, we show that filtering according to said high values improves the quality of predictions for a machine learning algorithm that detects transcription factor interactions based only on positional information. Finally, we provide a set of practices and guidelines, based on the signalValue feature, for scientists who wish to compare and merge narrowPeaks from ENCODE and Cistrome.

CONCLUSIONS

The signalValue feature is an informative feature that can be effectively used to highlight consistent areas of overlap between different sources of TF binding sites that expose it. Its applicability extends to downstream to positional machine learning algorithms, making it a powerful tool for performance tweaking and data aggregation.

Network analysis of three-dimensional hard-soft tissue relationships in the lower 1/3 of the face: skeletal Class I-normodivergent malocclusion versus Class II-hyperdivergent malocclusion

BACKGROUND

The determining effect of facial hard tissues on soft tissue morphology in orthodontic patients has yet to be explained. The aim of this study was to clarify the hard-soft tissue relationships of the lower 1/3 of the face in skeletal Class II-hyperdivergent patients compared with those in Class I-normodivergent patients using network analysis.

METHODS

Fifty-two adult patients (42 females, 10 males; age, 26.58 ± 5.80 years) were divided into two groups: Group 1, 25 subjects, skeletal Class I normodivergent pattern with straight profile; Group 2, 27 subjects, skeletal Class II hyperdivergent pattern with convex profile. Pretreatment cone-beam computed tomography and three-dimensional facial scans were taken and superimposed, on which landmarks were identified manually, and their coordinate values were used for network analysis.

RESULTS

(1) In sagittal direction, Group 2 correlations were generally weaker than Group 1. In both the vertical and sagittal directions of Group 1, the most influential hard tissue landmarks to soft tissues were located between the level of cemento-enamel junction of upper teeth and root apex of lower teeth. In Group 2, the hard tissue landmarks with the greatest influence in vertical direction were distributed more forward and downward than in Group 1. (2) In Group 1, all the correlations for vertical-hard tissue to sagittal-soft tissue position and sagittal-hard tissue to vertical-soft tissue position were positive. However, Group 2 correlations between vertical-hard tissue and sagittal-soft tissue positions were mostly negative. Between sagittal-hard tissue and vertical-soft tissue positions, Group 2 correlations were negative for mandible, and were positive for maxilla and teeth.

CONCLUSION

Compared with Class I normodivergent patients with straight profile, Class II hyperdivergent patients with convex profile had more variations in soft tissue morphology in sagittal direction. In vertical direction, the most relevant hard tissue landmarks on which soft tissue predictions should be based were distributed more forward and downward in Class II hyperdivergent patients with convex profile. Class II hyperdivergent pattern with convex profile was an imbalanced phenotype concerning sagittal and vertical positions of maxillofacial hard and soft tissues.

Integrative and comparative genomic analyses of mammalian macrophage responses to intracellular mycobacterial pathogens

Mycobacterium tuberculosis, the causative agent of human tuberculosis (hTB), is a close evolutionary relative of Mycobacterium bovis, which causes bovine tuberculosis (bTB), one of the most damaging infectious diseases to livestock agriculture. Previous studies have shown that the pathogenesis of bTB disease is comparable to hTB disease, and that the bovine and human alveolar macrophage (bAM and hAM, respectively) transcriptomes are extensively reprogrammed in response to infection with these intracellular mycobacterial pathogens. In this study, a multi-omics integrative approach was applied with functional genomics and GWAS data sets across the two primary hosts (Bos taurus and Homo sapiens) and both pathogens (M. bovis and M. tuberculosis). Four different experimental infection groups were used: 1) bAM infected with M. bovis, 2) bAM infected with M. tuberculosis, 3) hAM infected with M. tuberculosis, and 4) human monocyte-derived macrophages (hMDM) infected with M. tuberculosis. RNA-seq data from these experiments 24 h post-infection (24 hpi) was analysed using three computational pipelines: 1) differentially expressed genes, 2) differential gene expression interaction networks, and 3) combined pathway analysis. The results were integrated with high-resolution bovine and human GWAS data sets to detect novel quantitative trait loci (QTLs) for resistance to mycobacterial infection and resilience to disease. This revealed common and unique response macrophage pathways for both pathogens and identified 32 genes (12 bovine and 20 human) significantly enriched for SNPs associated with disease resistance, the majority of which encode key components of the NF-κB signalling pathway and that also drive formation of the granuloma.

Emergent scale-free networks

Many complex systems-from the Internet to social, biological, and communication networks-are thought to exhibit scale-free structure. However, prevailing explanations require that networks grow over time, an assumption that fails in some real-world settings. Here, we explain how scale-free structure can emerge without growth through network self-organization. Beginning with an arbitrary network, we allow connections to detach from random nodes and then reconnect under a mixture of preferential and random attachment. While the numbers of nodes and edges remain fixed, the degree distribution evolves toward a power-law with an exponent γ = 1 + 1 p that depends only on the proportion p of preferential (rather than random) attachment. Applying our model to several real networks, we infer p directly from data and predict the relationship between network size and degree heterogeneity. Together, these results establish how scale-free structure can arise in networks of constant size and density, with broad implications for the structure and function of complex systems.

Network Models of BACE-1 Inhibitors: Exploring Structural and Biochemical Relationships

This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.

SAILoR: Structure-Aware Inference of Logic Rules

Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The number of experiments is usually much smaller than the number of genes. In addition, binarization leads to a loss of information and inconsistencies arise in binarized time-series data. The inference of Boolean networks from binarized time-series data alone often leads to complex and overfitted models. To obtain relevant Boolean models of gene regulatory networks, inference methods could incorporate data from multiple sources and prior knowledge in terms of general network structure and/or exact interactions. We propose the Boolean network inference method SAILoR (Structure-Aware Inference of Logic Rules). SAILoR incorporates time-series gene expression data in combination with provided reference networks to infer accurate Boolean models. SAILoR automatically extracts topological properties from reference networks. These can describe a more general structure of the GRN or can be more precise and describe specific interactions. SAILoR infers a Boolean network by learning from both continuous and binarized time-series data. It navigates between two main objectives, topological similarity to reference networks and correspondence with gene expression data. By incorporating the NSGA-II multi-objective genetic algorithm, SAILoR relies on the wisdom of crowds. Our results indicate that SAILoR can infer accurate and biologically relevant Boolean descriptions of GRNs from both a static and a dynamic perspective. We show that SAILoR improves the static accuracy of the inferred network compared to the network inference method dynGENIE3. Furthermore, we compared the performance of SAILoR with other Boolean network inference approaches including Best-Fit, REVEAL, MIBNI, GABNI, ATEN, and LogBTF. We have shown that by incorporating prior knowledge about the overall network structure, SAILoR can improve the structural correctness of the inferred Boolean networks while maintaining dynamic accuracy. To demonstrate the applicability of SAILoR, we inferred context-specific Boolean subnetworks of female Drosophila melanogaster before and after mating.

Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information

Knowledge graph embeddings (KGE) are a powerful technique used in the biomedical domain to represent biological knowledge in a low dimensional space. However, a deep understanding of these methods is still missing, and, in particular, regarding their applications to prioritize genes associated with complex diseases with reduced genetic information. In this contribution, we built a knowledge graph (KG) by integrating heterogeneous biomedical data and generated KGE by implementing state-of-the-art methods, and two novel algorithms: Dlemb and BioKG2vec. Extensive testing of the embeddings with unsupervised clustering and supervised methods showed that KGE can be successfully implemented to predict genes associated with diseases and that our novel approaches outperform most existing algorithms in both scenarios. Our findings underscore the significance of data quality, preprocessing, and integration in achieving accurate predictions. Additionally, we applied KGE to predict genes linked to Intervertebral Disc Degeneration (IDD) and illustrated that functions pertinent to the disease are enriched within the prioritized gene set.

More than two components: complexities in bacterial phosphosignaling

For over 40 years, the two-component systems (TCSs) have taken front and center in our thinking about the signaling mechanisms by which bacteria sense and respond to their environment. In contrast, phosphorylation on Ser/Thr and Tyr (O-phosphorylation) was long thought to be mostly restricted to eukaryotes and a somewhat accessory signaling mechanism in bacteria. Several recent studies exploring systems aspects of bacterial O-phosphorylation, however, now show that it is in fact pervasive, with some bacterial proteomes as highly phosphorylated as those of eukaryotes. Labile, non-canonical protein phosphorylation sites on Asp, Arg, and His are now also being identified in large numbers in bacteria and first cellular functions are discovered. Other phosphomodifications on Cys, Glu, and Lys remain largely unexplored. The surprising breadth and complexity of bacterial phosphosignaling reveals a vast signaling capacity, the full scope of which we may only now be beginning to understand but whose functions are likely to affect all aspects of bacterial physiology and pathogenesis.

Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data

MOTIVATION

In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs.

RESULTS

To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest.

AVAILABILITY AND IMPLEMENTATION

STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444.

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human Transcription factors (TFs) genes encode multiple protein isoforms differing in DNA binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators", both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Optimizing therapeutic targets for breast cancer using boolean network models

Studying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising from the interconnections between network components grows exponentially. In this study, using Boolean logic to adjust the existing relationships between network components has facilitated simplifying the modeling process, enabling the generation of attractors that represent cell phenotypes based on breast cancer RNA-seq data. A key therapeutic objective is to guide cells, through targeted interventions, to transition from the current cancer attractor to a physiologically distinct attractor unrelated to cancer. To achieve this, we developed a computational method that identifies network nodes whose inhibition can facilitate the desired transition from one tumor attractor to another associated with apoptosis, leveraging transcriptomic data from cell lines. To validate the model, we utilized previously published in vitro experiments where the downregulation of specific proteins resulted in cell growth arrest and death of a breast cancer cell line. The method proposed in this manuscript combines diverse data sources, conducts structural network analysis, and incorporates relevant biological knowledge on apoptosis in cancer cells. This comprehensive approach aims to identify potential targets of significance for personalized medicine.

How Network Structure Shapes Languages: Disentangling the Factors Driving Variation in Communicative Agents

Languages show substantial variability between their speakers, but it is currently unclear how the structure of the communicative network contributes to the patterning of this variability. While previous studies have highlighted the role of network structure in language change, the specific aspects of network structure that shape language variability remain largely unknown. To address this gap, we developed a Bayesian agent-based model of language evolution, contrasting between two distinct scenarios: language change and language emergence. By isolating the relative effects of specific global network metrics across thousands of simulations, we show that global characteristics of network structure play a critical role in shaping interindividual variation in language, while intraindividual variation is relatively unaffected. We effectively challenge the long-held belief that size and density are the main network structural factors influencing language variation, and show that path length and clustering coefficient are the main factors driving interindividual variation. In particular, we show that variation is more likely to occur in populations where individuals are not well-connected to each other. Additionally, variation is more likely to emerge in populations that are structured in small communities. Our study provides potentially important insights into the theoretical mechanisms underlying language variation.

Application of a mathematical model to clarify the statistical characteristics of a pan-tissue DNA methylation clock

DNA methylation clocks estimate biological age based on DNA methylation profiles. This study developed a mathematical model to describe DNA methylation aging and the establishment of a pan-tissue DNA methylation clock. The model simulates the aging dynamics of DNA methylation profiles based on passive demethylation as well as the process of cross-sectional bulk data acquisition. As a result, this study identified two conditions under which the pan-tissue DNA methylation clock can successfully predict biological age: one condition is that the target tissues are sufficiently well represented in the training dataset, and the other condition is that the target sample contains cell subsets that are common among different tissues. When either of these conditions is met, the clock performs well. It is also suggested that the epigenetic age of all samples in the target tissue tends to be either over or underestimated when biological age prediction fails. The model can reveal the statistical characteristics of DNA methylation clocks.

Preponderance of generalized chain functions in reconstructed Boolean models of biological networks

Boolean networks (BNs) have been extensively used to model gene regulatory networks (GRNs). The dynamics of BNs depend on the network architecture and regulatory logic rules (Boolean functions (BFs)) associated with nodes. Nested canalyzing functions (NCFs) have been shown to be enriched among the BFs in the large-scale studies of reconstructed Boolean models. The central question we address here is whether that enrichment is due to certain sub-types of NCFs. We build on one sub-type of NCFs, the chain functions (or chain-0 functions) proposed by Gat-Viks and Shamir. First, we propose two other sub-types of NCFs, namely, the class of chain-1 functions and generalized chain functions, the union of the chain-0 and chain-1 types. Next, we find that the fraction of NCFs that are chain-0 (also holds for chain-1) functions decreases exponentially with the number of inputs. We provide analytical treatment for this and other observations on BFs. Then, by analyzing three different datasets of reconstructed Boolean models we find that generalized chain functions are significantly enriched within the NCFs. Lastly we illustrate that upon imposing the constraints of generalized chain functions on three different GRNs we are able to obtain biologically viable Boolean models.

Prediction of Essential Proteins of Klebsiella pneumoniae using Integrative Bioinformatics and Systems Biology Approach: Unveiling New Avenues for Drug Discovery

Klebsiella pneumoniae is an opportunistic multidrug-resistant bacterial pathogen responsible for various health care-associated infections. The prediction of proteins that are essential for the survival of bacterial pathogens can greatly facilitate the drug development and discovery pipeline toward target identification. To this end, the present study reports a comprehensive computational approach integrating bioinformatics and systems biology-based methods to identify essential proteins of K. pneumoniae involved in vital processes. From the proteome of this pathogen, we predicted a total of 854 essential proteins based on sequence, protein-protein interaction (PPI) and genome-scale metabolic model methods. These predicted essential proteins are involved in vital processes for cellular regulation such as translation, metabolism, and biosynthesis of essential factors, among others. Cluster analysis of the PPI network revealed the highly connected modules involved in the basic functionality of the organism. Further, the predicted consensus set of essential proteins of K. pneumoniae was evaluated by comparing them with existing resources (NetGenes and PATHOgenex) and literature. The findings of this study offer guidance toward understanding cell functionality, thereby facilitating the understanding of pathogen systems and providing a way forward to shortlist potential therapeutic candidates for developing novel antimicrobial agents against K. pneumoniae. In addition, the research strategy presented herein is a fusion of sequence and systems biology-based approaches that offers prospects as a model to predict essential proteins for other pathogens.

Combination of computational new approach methodologies for enhancing evidence of biological pathway conservation across species

The ability to predict which chemicals are of concern for environmental safety is dependent, in part, on the ability to extrapolate chemical effects across many species. This work investigated the complementary use of two computational new approach methodologies to support cross-species predictions of chemical susceptibility: the US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool and Unilever's recently developed Genes to Pathways - Species Conservation Analysis (G2P-SCAN) tool. These stand-alone tools rely on existing biological knowledge to help understand chemical susceptibility and biological pathway conservation across species. The utility and challenges of these combined computational approaches were demonstrated using case examples focused on chemical interactions with peroxisome proliferator activated receptor alpha (PPARα), estrogen receptor 1 (ESR1), and gamma-aminobutyric acid type A receptor subunit alpha (GABRA1). Overall, the biological pathway information enhanced the weight of evidence to support cross-species susceptibility predictions. Through comparisons of relevant molecular and functional data gleaned from adverse outcome pathways (AOPs) to mapped biological pathways, it was possible to gain a toxicological context for various chemical-protein interactions. The information gained through this computational approach could ultimately inform chemical safety assessments by enhancing cross-species predictions of chemical susceptibility. It could also help fulfill a core objective of the AOP framework by potentially expanding the biologically plausible taxonomic domain of applicability of relevant AOPs.

Phenotypic plasticity for improved light harvesting, in tandem with methylome repatterning in reef-building corals

Acclimatization through phenotypic plasticity represents a more rapid response to environmental change than adaptation and is vital to optimize organisms' performance in different conditions. Generally, animals are less phenotypically plastic than plants, but reef-building corals exhibit plant-like properties. They are light dependent with a sessile and modular construction that facilitates rapid morphological changes within their lifetime. We induced phenotypic changes by altering light exposure in a reciprocal transplant experiment and found that coral plasticity is a colony trait emerging from comprehensive morphological and physiological changes within the colony. Plasticity in skeletal features optimized coral light harvesting and utilization and paralleled significant methylome and transcriptome modifications. Network-associated responses resulted in the identification of hub genes and clusters associated to the change in phenotype: inter-partner recognition and phagocytosis, soft tissue growth and biomineralization. Furthermore, we identified hub genes putatively involved in animal photoreception-phototransduction. These findings fundamentally advance our understanding of how reef-building corals repattern the methylome and adjust a phenotype, revealing an important role of light sensing by the coral animal to optimize photosynthetic performance of the symbionts.

Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks

Background

Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks.

Results

We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples.

Conclusion

This study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.

Harnessing regulatory networks in Actinobacteria for natural product discovery

Microbes typically live in complex habitats where they need to rapidly adapt to continuously changing growth conditions. To do so, they produce an astonishing array of natural products with diverse structures and functions. Actinobacteria stand out for their prolific production of bioactive molecules, including antibiotics, anticancer agents, antifungals, and immunosuppressants. Attention has been directed especially towards the identification of the compounds they produce and the mining of the large diversity of biosynthetic gene clusters (BGCs) in their genomes. However, the current return on investment in random screening for bioactive compounds is low, while it is hard to predict which of the millions of BGCs should be prioritized. Moreover, many of the BGCs for yet undiscovered natural products are silent or cryptic under laboratory growth conditions. To identify ways to prioritize and activate these BGCs, knowledge regarding the way their expression is controlled is crucial. Intricate regulatory networks control global gene expression in Actinobacteria, governed by a staggering number of up to 1000 transcription factors per strain. This review highlights recent advances in experimental and computational methods for characterizing and predicting transcription factor binding sites and their applications to guide natural product discovery. We propose that regulation-guided genome mining approaches will open new avenues toward eliciting the expression of BGCs, as well as prioritizing subsets of BGCs for expression using synthetic biology approaches.

ONE-SENTENCE SUMMARY

This review provides insights into advances in experimental and computational methods aimed at predicting transcription factor binding sites and their applications to guide natural product discovery.

Immune-Related Molecules CD3G and FERMT3: Novel Biomarkers Associated with Sepsis

Sepsis ranks among the most common health problems worldwide, characterized by organ dysfunction resulting from infection. Excessive inflammatory responses, cytokine storms, and immune-induced microthrombosis are pivotal factors influencing the progression of sepsis. Our objective was to identify novel immune-related hub genes for sepsis through bioinformatic analysis, subsequently validating their specificity and potential as diagnostic and prognostic biomarkers in an animal experiment involving a sepsis mice model. Gene expression profiles of healthy controls and patients with sepsis were obtained from the Gene Expression Omnibus (GEO) and analysis of differentially expressed genes (DEGs) was conducted. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to analyze genes within crucial modules. The functional annotated DEGs which related to the immune signal pathways were used for constructing protein-protein interaction (PPI) analysis. Following this, two hub genes, FERMT3 and CD3G, were identified through correlation analyses associated with sequential organ failure assessment (SOFA) scores. These two hub genes were associated with cell adhesion, migration, thrombosis, and T-cell activation. Furthermore, immune infiltration analysis was conducted to investigate the inflammation microenvironment influenced by the hub genes. The efficacy and specificity of the two hub genes were validated through a mice sepsis model study. Concurrently, we observed a significant negative correlation between the expression of CD3G and IL-1β and GRO/KC. These findings suggest that these two genes probably play important roles in the pathogenesis and progression of sepsis, presenting the potential to serve as more stable biomarkers for sepsis diagnosis and prognosis, deserving further study.

A machine learning and directed network optimization approach to uncover TP53 regulatory patterns

TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53 from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine.

Systematical analyses of large-scale transcriptome reveal viral infection-related genes and disease comorbidities

Perturbation of transcriptome in viral infection patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent transcriptome and identification of robust biomarkers is not complete. In this study, we manually collected 23 datasets related to 6,197 blood transcriptomes across 16 types of respiratory virus infections. We applied a comprehensive systems biology approach starting with whole-blood transcriptomes combined with multilevel bioinformatics analyses to characterize the expression, functional pathways, and protein-protein interaction (PPI) networks to identify robust biomarkers and disease comorbidities. Robust gene markers of infection with different viruses were identified, which can accurately classify the normal and infected patients in train and validation cohorts. The biological processes (BP) of different viruses showed great similarity and enriched in infection and immune response pathways. Network-based analyses revealed that a variety of viral infections were associated with nervous system diseases, neoplasms and metabolic diseases, and significantly correlated with brain tissues. In summary, our manually collected transcriptomes and comprehensive analyses reveal key molecular markers and disease comorbidities in the process of viral infection, which could provide a valuable theoretical basis for the prevention of subsequent public health events for respiratory virus infections.

Imaging immune checkpoint networks in cancer tissues with supermultiplexed SERS nanoprobes

Combined immune checkpoint (ICP) inhibitors maximize immune response rates of patients compared to the single-drug treatment strategy in cancer immunotherapy, and prediction of such optimal combinations requires high-throughput imaging techniques and suitable data analysis. In this work, we report a rational strategy for predicting combined drugs of ICP inhibitors based on supermultiplexed surface-enhanced Raman scattering (SERS) imaging and correlation network analysis. To this end, we first built an ultrasensitive and supermultiplexed volume-active SERS (VASERS) nanoprobe platform, where Raman molecules are randomly arranged in 3D volumetric electromagnetic hotspots. By examining various bio-orthogonal Raman molecules with different electronic properties, we developed frequency modulation guidelines and achieved 32 resolvable colors in the Raman-silent region, the largest number of resolvable SERS colors demonstrated to date. We then demonstrated one-shot ten-color imaging of ICPs with high spectral resolution in clinical biopsies of breast cancer tissues, suggesting highly heterogeneous expression patterns of ICPs across tumor subtypes. Through correlation network analysis of these high-throughput Raman data, we investigated co-expression relationships among these ten-panel ICPs in cancer tissues and finally identified a variety of possible ICP combinations for synergistic immunotherapy of breast cancers, which may lead to novel therapeutical insights.

The duplication of genomes and genetic networks and its potential for evolutionary adaptation and survival during environmental turmoil

The importance of whole-genome duplication (WGD) for evolution is controversial. Whereas some view WGD mainly as detrimental and an evolutionary dead end, there is growing evidence that polyploidization can help overcome environmental change, stressful conditions, or periods of extinction. However, despite much research, the mechanistic underpinnings of why and how polyploids might be able to outcompete or outlive nonpolyploids at times of environmental upheaval remain elusive, especially for autopolyploids, in which heterosis effects are limited. On the longer term, WGD might increase both mutational and environmental robustness due to redundancy and increased genetic variation, but on the short-or even immediate-term, selective advantages of WGDs are harder to explain. Here, by duplicating artificially generated Gene Regulatory Networks (GRNs), we show that duplicated GRNs-and thus duplicated genomes-show higher signal output variation than nonduplicated GRNs. This increased variation leads to niche expansion and can provide polyploid populations with substantial advantages to survive environmental turmoil. In contrast, under stable environments, GRNs might be maladaptive to changes, a phenomenon that is exacerbated in duplicated GRNs. We believe that these results provide insights into how genome duplication and (auto)polyploidy might help organisms to adapt quickly to novel conditions and to survive ecological uproar or even cataclysmic events.

Identification of milk-related genes and regulatory networks in Bactrian camel either supplemented or under grazing

Using gene co-expression networks to understand dynamic characterizations in lactating animals becomes a common method. However, there are rarely reporters focusing on milk traits in Bactrian camel by high-throughput sequencing. We used RNA-seq to generate the camel transcriptome from the blood of 16 lactating Alxa Bactrian camel in different feeding groups. In total, we obtained 1185 milk-related genes correlated with milk yield, milk protein, milk fat, and milk lactose across the WGCNA analysis. Moreover, 364 milk-related genes were differentially expressed between supplementation and grazing feeding groups. The differential expression-camel milk-related genes CMRGs (DE-CMRGs) in supplement direct an intensive gene co-expression network to improve milk performance in lactating camels. This study provides a non-invasive method to identify the camel milk-related genes in camel blood for four primary milk traits and valuable theoretical basis and research ideas for the study of the milk performance regulation mechanism of camelid animals.

Transcriptional profiling of canine osteosarcoma identifies prognostic gene expression signatures with translational value for humans

Canine osteosarcoma is increasingly recognized as an informative model for human osteosarcoma. Here we show in one of the largest clinically annotated canine osteosarcoma transcriptional datasets that two previously reported, as well as de novo gene signatures devised through single sample Gene Set Enrichment Analysis (ssGSEA), have prognostic utility in both human and canine patients. Shared molecular pathway alterations are seen in immune cell signaling and activation including TH1 and TH2 signaling, interferon signaling, and inflammatory responses. Virtual cell sorting to estimate immune cell populations within canine and human tumors showed similar trends, predominantly for macrophages and CD8+ T cells. Immunohistochemical staining verified the increased presence of immune cells in tumors exhibiting immune gene enrichment. Collectively these findings further validate naturally occurring osteosarcoma of the pet dog as a translationally relevant patient model for humans and improve our understanding of the immunologic and genomic landscape of the disease in both species.

Gene regulatory network inference in soybean upon infection by Phytophthora sojae

Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean (Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection by P. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis (Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response against P. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance to P. sojae.

Scaling of nuclear numbers and their spatial arrangement in skeletal muscle cell size regulation

Many cells display considerable functional plasticity and depend on the regulation of numerous organelles and macromolecules for their maintenance. In large cells, organelles also need to be carefully distributed to supply the cell with essential resources and regulate intracellular activities. Having multiple copies of the largest eukaryotic organelle, the nucleus, epitomizes the importance of scaling gene products to large cytoplasmic volumes in skeletal muscle fibers. Scaling of intracellular constituents within mammalian muscle fibers is, however, poorly understood, but according to the myonuclear domain hypothesis, a single nucleus supports a finite amount of cytoplasm and is thus postulated to act autonomously, causing the nuclear number to be commensurate with fiber volume. In addition, the orderly peripheral distribution of myonuclei is a hallmark of normal cell physiology, as nuclear mispositioning is associated with impaired muscle function. Because underlying structures of complex cell behaviors are commonly formalized by scaling laws and thus emphasize emerging principles of size regulation, the work presented herein offers more of a unified conceptual platform based on principles from physics, chemistry, geometry, and biology to explore cell size-dependent correlations of the largest mammalian cell by means of scaling.

The critical node detection problem in hypergraphs using weighted node degree centrality

Network analysis is an indispensable part of today's academic field. Among the different types of networks, the more complex hypergraphs can provide an excellent challenge and new angles for analysis. This study proposes a variant of the critical node detection problem for hypergraphs using weighted node degree centrality as a form of importance metric. An analysis is done on both generated synthetic networks and real-world derived data on the topic of United States House and Senate committees, using a newly designed algorithm. The numerical results show that the combination of the critical node detection on hypergraphs with the weighted node degree centrality provides promising results and the topic is worth exploring further.

ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology

The ability to quantify structural changes of the endoplasmic reticulum (ER) is crucial for understanding the structure and function of this organelle. However, the rapid movement and complex topology of ER networks make this challenging. Here, we construct a state-of-the-art semantic segmentation method that we call ERnet for the automatic classification of sheet and tubular ER domains inside individual cells. Data are skeletonized and represented by connectivity graphs, enabling precise and efficient quantification of network connectivity. ERnet generates metrics on topology and integrity of ER structures and quantifies structural change in response to genetic or metabolic manipulation. We validate ERnet using data obtained by various ER-imaging methods from different cell types as well as ground truth images of synthetic ER structures. ERnet can be deployed in an automatic high-throughput and unbiased fashion and identifies subtle changes in ER phenotypes that may inform on disease progression and response to therapy.

FinO/ProQ-family proteins: an evolutionary perspective

RNA-binding proteins are key actors of post-transcriptional networks. Almost exclusively studied in the light of their interactions with RNA ligands and the associated functional events, they are still poorly understood as evolutionary units. In this review, we discuss the FinO/ProQ family of bacterial RNA chaperones, how they evolve and spread across bacterial populations and what properties and opportunities they provide to their host cells. We reflect on major conserved and divergent themes within the family, trying to understand how the same ancestral RNA-binding fold, augmented with additional structural elements, could yield either highly specialised proteins or, on the contrary, globally acting regulatory hubs with a pervasive impact on gene expression. We also consider dominant convergent evolutionary trends that shaped their RNA chaperone activity and recurrently implicated the FinO/ProQ-like proteins in bacterial DNA metabolism, translation and virulence. Finally, we offer a new perspective in which FinO/ProQ-family regulators emerge as active evolutionary players with both negative and positive roles, significantly impacting the evolutionary modes and trajectories of their bacterial hosts.

Osteochondrogenesis by TGF-β3, BMP-2 and noggin growth factor combinations in an ex vivo muscle tissue model: Temporal function changes affecting tissue morphogenesis

In the absence of clear molecular insight, the biological mechanism behind the use of growth factors applied in osteochondral regeneration is still unresolved. The present study aimed to resolve whether multiple growth factors applied to muscle tissue in vitro, such as TGF-β3, BMP-2 and Noggin, can lead to appropriate tissue morphogenesis with a specific osteochondrogenic nature, thereby revealing the underlying molecular interaction mechanisms during the differentiation process. Interestingly, although the results showed the typical modulatory effect of BMP-2 and TGF-β3 on the osteochondral process, and Noggin seemingly downregulated specific signals such as BMP-2 activity, we also discovered a synergistic effect between TGF-β3 and Noggin that positively influenced tissue morphogenesis. Noggin was observed to upregulate BMP-2 and OCN at specific time windows of culture in the presence of TGF-β3, suggesting a temporal time switch causing functional changes in the signaling protein. This implies that signals change their functions throughout the process of new tissue formation, which may depend on the presence or absence of specific singular or multiple signaling cues. If this is the case, the signaling cascade is far more intricate and complex than originally believed, warranting intensive future investigations so that regenerative therapies of a critical clinical nature can function properly.

Leveraging quantum computing for dynamic analyses of logical networks in systems biology

The dynamics of cellular mechanisms can be investigated through the analysis of networks. One of the simplest but most popular modeling strategies involves logic-based models. However, these models still face exponential growth in simulation complexity compared with a linear increase in nodes. We transfer this modeling approach to quantum computing and use the upcoming technique in the field to simulate the resulting networks. Leveraging logic modeling in quantum computing has many benefits, including complexity reduction and quantum algorithms for systems biology tasks. To showcase the applicability of our approach to systems biology tasks, we implemented a model of mammalian cortical development. Here, we applied a quantum algorithm to estimate the tendency of the model to reach particular stable conditions and further revert dynamics. Results from two actual quantum processing units and a noisy simulator are presented, and current technical challenges are discussed.

Effective Reversal of Macrophage Polarization by Inhibitory Combinations Predicted by a Boolean Protein–Protein Interaction Model

Background: The function and polarization of macrophages has a significant impact on the outcome of many diseases. Targeting tumor-associated macrophages (TAMs) is among the greatest challenges to solve because of the low in vitro reproducibility of the heterogeneous tumor microenvironment (TME). To create a more comprehensive model and to understand the inner workings of the macrophage and its dependence on extracellular signals driving polarization, we propose an in silico approach. Methods: A Boolean control network was built based on systematic manual curation of the scientific literature to model the early response events of macrophages by connecting extracellular signals (input) with gene transcription (output). The network consists of 106 nodes, classified as 9 input, 75 inner and 22 output nodes, that are connected by 217 edges. The direction and polarity of edges were manually verified and only included in the model if the literature plainly supported these parameters. Single or combinatory inhibitions were simulated mimicking therapeutic interventions, and output patterns were analyzed to interpret changes in polarization and cell function. Results: We show that inhibiting a single target is inadequate to modify an established polarization, and that in combination therapy, inhibiting numerous targets with individually small effects is frequently required. Our findings show the importance of JAK1, JAK3 and STAT6, and to a lesser extent STK4, Sp1 and Tyk2, in establishing an M1-like pro-inflammatory polarization, and NFAT5 in creating an anti-inflammatory M2-like phenotype. Conclusions: Here, we demonstrate a protein–protein interaction (PPI) network modeling the intracellular signalization driving macrophage polarization, offering the possibility of therapeutic repolarization and demonstrating evidence for multi-target methods.

Identification of Prognosis-Related Oxidative Stress Model with Immunosuppression in HCC

For hepatocellular carcinoma (HCC) patients, we attempted to establish a new oxidative stress (OS)-related prognostic model for predicting prognosis, exploring immune microenvironment, and predicting the immunotherapy response. Significantly differently expressed oxidative stress-related genes (DEOSGs) between normal and HCC samples from the Cancer Genome Atlas (TCGA) were screened, and then based on weighted gene coexpression network analysis (WGCNA), HCC-related hub genes were discovered. Based on the least absolute shrinkage and selection operator (LASSO) and cox regression analysis, a prognostic model was developed. We validated the prognostic model's predictive power using an external validation cohort: the International Cancer Genome Consortium (ICGC).Then a nomogram was determined. Furthermore, we also examined the relationship of the risk model and clinical characteristics as well as immune microenvironment. 434 DEOSGs, comprising 62 downregulated and 372 upregulated genes (p < 0.05 and |log2FC| ≥ 1), and 257 HCC-related hub genes were recognized in HCC. Afterward, we built a five-DEOSG (LOX, CYP2C9, EIF2B4, EZH2, and SRXN1) prognostic risk model. Using the nomogram, the risk model was shown to have good prognostic value. Compared to the low risk group, HCC patients with high risk had poorer outcomes, worse pathological grades, and advanced tumor stages (p < 0.05). There were significant increases in LOX, EIF2B4, EZH2, and SRXN1 expression in HCC samples, while CYP2C9 expression was decreased. Finally, Real-time PCR (RT-qPCR) confirmed the mRNA expressions of five genes (CYP2C9, EIF2B4, EZH2, SRXN1, LOX) in HCC cell lines. Our study constructed a prognostic OS-related model with strong predictive power and potential as an immunosuppressive biomarker for HCC leading to improving prediction and providing new insights for HCC immunotherapy.

Phosphorylation of the Hsp90 Co-Chaperone Hop Changes its Conformational Dynamics and Biological Function

The molecular chaperones Hsp90 and Hsp70 and their regulatory co-chaperone Hop play a key role at the crossroads of the folding pathways of numerous client proteins by forming fine-tuned multiprotein complexes. Alterations of the biomolecules involved may functionally impact the chaperone machinery: here, we integrate simulations and experiments to unveil how Hop conformational fitness and interactions can be controlled by the perturbation of just one residue. Specifically, we unveil how mechanisms mediated by Hop residue Y354 control Hop open and closed states, which affect binding of Hsp70/Hsp90. Phosphorylation or mutation of Hop-Y354 are shown to favor structural ensembles that are indeed not optimal for stable interactions with Hsp90 and Hsp70. This disfavors cellular accumulation of the stringent Hsp90 clients glucocorticoid receptor and the viral tyrosine kinase v-Src, with detrimental effects on v-Src activity. Our results show how the post-translational modification of a specific residue in Hop provides a regulation mechanism for the larger chaperone complex of which it is part. In this framework, the effects of one single alteration are amplified at the cellular level through the perturbation of protein-interaction networks.

Resonant learning in scale-free networks

Large networks of interconnected components, such as genes or machines, can coordinate complex behavioral dynamics. One outstanding question has been to identify the design principles that allow such networks to learn new behaviors. Here, we use Boolean networks as prototypes to demonstrate how periodic activation of network hubs provides a network-level advantage in evolutionary learning. Surprisingly, we find that a network can simultaneously learn distinct target functions upon distinct hub oscillations. We term this emergent property resonant learning, as the new selected dynamical behaviors depend on the choice of the period of the hub oscillations. Furthermore, this procedure accelerates the learning of new behaviors by an order of magnitude faster than without oscillations. While it is well-established that modular network architecture can be selected through evolutionary learning to produce different network behaviors, forced hub oscillations emerge as an alternative evolutionary learning strategy for which network modularity is not necessarily required.